Tools

"... Message passing algorithms are some newly developed algorithms in context of telecommunication engineering in which one can perform the algorithm over different parallel machines. The first use of these algorithms were in the context of graphical models for computing the marginal pdf of some random ..."

Messagepassingalgorithms are some newly developed algorithms in context of telecommunication engineering in which one can perform the algorithm over different parallel machines. The first use of these algorithms were in the context of graphical models for computing the marginal pdf of some random

"... Message passing over factor graph can be considered as generalization of many well known algorithms for efficient marginalization of multivariate function. A specific instance of the algorithm is obtained by choosing an appropriate commutative semiring for the range of the function to be marginalize ..."

Messagepassing over factor graph can be considered as generalization of many well known algorithms for efficient marginalization of multivariate function. A specific instance of the algorithm is obtained by choosing an appropriate commutative semiring for the range of the function

"... Recent developments in compressive sensing (CS) combined with increasing demands for effective high-dimensional inference techniques across a variety of disciplines have motivated extensive research into algorithms exploiting various notions of parsimony, including sparsity and low-rank constraints. ..."

. In this dissertation, we extend the generalized approximate messagepassing (GAMP) approach, originally proposed for high-dimensional generalized-linear regression in the context of CS, to handle several classes of bilinear inference problems. First, we consider a general form of noisy CS where there is uncertainty

"... Gaussian belief propagation (GaBP) is an iterative algorithm for computing the mean (and variances) of a multivariate Gaussian distribution, or equivalently, the minimum of a multivariate positive definite quadratic function. Sufficient conditions, such as walk-summability, that guarantee the conver ..."

Gaussian belief propagation (GaBP) is an iterative algorithm for computing the mean (and variances) of a multivariate Gaussian distribution, or equivalently, the minimum of a multivariate positive definite quadratic function. Sufficient conditions, such as walk-summability, that guarantee

"... Governments, universities, and companies expend vast resources building the top supercomputers. The processors and interconnect networks become faster, while the number of nodes grows exponentially. Problems of scale emerge, not least of which is collective performance. This thesis identifies and pr ..."

"... Abstract — A new learning scenario, Transfer Learning (TL) has improves learning performance when the data can be in different feature spaces and where no correspondence between data instances in these spaces is provided. Transfer learning has been used to present image clustering as an example to i ..."

to illustrate how unsupervised learning can be improved by transferring knowledge from auxiliary data. This paper proposes a new and far more efficient method of Transfer Learning for MessagePassingAlgorithm. Based on this they cluster a small collection of images using readily available annotated image data

"... Continuously-Adaptive Discretization for Message-Passing (CAD-MP) is a new message-passing algorithm for approximate inference. Most message-passing algorithms approximate continuous probability distributions using either: a family of continuous distributions such as the exponential family; a partic ..."

Continuously-Adaptive Discretization for Message-Passing (CAD-MP) is a new message-passingalgorithm for approximate inference. Most message-passingalgorithms approximate continuous probability distributions using either: a family of continuous distributions such as the exponential family; a

"... Abstract—We consider the recovery of a nonnegative vector x from measurements y = Ax, where A ∈ {0, 1}m×n. We establish that when A corresponds to the adjacency matrix of a bipartite graph with sufficient expansion, a simple message-passing algo-rithm produces an estimate x ̂ of x satisfying ‖x−x̂‖1 ..."

Abstract—We consider the recovery of a nonnegative vector x from measurements y = Ax, where A ∈ {0, 1}m×n. We establish that when A corresponds to the adjacency matrix of a bipartite graph with sufficient expansion, a simple message-passingalgo-rithm produces an estimate x ̂ of x satisfying ‖x

"... Abstract. Message-passing algorithms (MPAs) are an algorithmic para-digm for the following generic problem: given a system consisting of sev-eral interacting components, compute a new version of each component representing its behaviour inside the system. MPAs avoid computing the full state space by ..."

Abstract. Message-passingalgorithms (MPAs) are an algorithmic para-digm for the following generic problem: given a system consisting of sev-eral interacting components, compute a new version of each component representing its behaviour inside the system. MPAs avoid computing the full state space